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20/08/2026 6:09 pm
TL;DR: LLM gateways simplify multi-model application development by unifying routing, failover, caching, and observability across providers, according to Braintrust, but they also centralise authentication, request handling, and audit visibility in ways that security teams must govern. The governance question is no longer whether a gateway works, but whether it creates a controllable identity and data boundary for production AI.
NHIMG editorial — based on content published by Braintrust: 6 best LLM gateways for developers in 2026
By the numbers:
- Portkey's enterprise tier supports 1,600+ models, showing how quickly gateway sprawl can become an operational governance problem.
- OpenRouter provides access to 500+ models across 60+ providers through one API key, which illustrates how concentrated access can mask governance fragmentation.
Questions worth separating out
Q: What breaks when an LLM gateway becomes the default trust point for AI apps?
A: When an LLM gateway becomes the default trust point, one API key or service token can inherit access to multiple model providers, caches, and telemetry streams.
Q: Why do LLM gateways create new governance issues for AI teams?
A: They collapse routing, authentication, logging, and caching into one layer, which makes them efficient but also harder to govern.
Q: How do security teams know whether gateway observability is actually helping?
A: Observability is working when traces, evaluations, and regression checks consistently identify model drift, faulty prompts, or risky provider behaviour before users are affected.
Practitioner guidance
- Inventory every gateway credential and downstream provider trust path Map which API keys, service accounts, and vendor tokens can reach each model provider through the gateway, then classify them by privilege and data sensitivity.
- Separate routing access from observability access Do not assume teams that can route requests should also be able to read traces, prompts, or evaluation outputs.
- Set explicit cache handling rules for prompts and outputs Require encryption, TTL limits, and scope restrictions for cached model responses, especially where prompts may contain secrets, personal data, or customer context.
What's in the full article
Braintrust's full analysis covers the operational detail this post intentionally leaves for the source:
- Cross-provider routing configuration for OpenAI, Anthropic, Google, AWS Bedrock, Vertex AI, and Azure
- Built-in tracing and evaluation workflow details for production request analysis and regression testing
- Encrypted caching behaviour, including cache-control headers, TTL handling, and per-request configuration
- Platform-specific deployment and beta limitations that matter for implementation planning
👉 Read Braintrust's full review of the best LLM gateways for developers in 2026 →
LLM gateways and AI stack governance: what are teams missing?
Explore further
20/08/2026 6:45 pm
LLM gateways are becoming identity control points, not just API routers. The article makes clear that a single gateway can front multiple providers, hide the complexity of SDK sprawl, and centralise authentication into one key or token. That creates an identity boundary that behaves more like a workload identity broker than a developer utility. For practitioners, the challenge is to govern that trust concentration before it becomes the default access pattern across AI applications.
A question worth separating out:
Q: When should organisations separate routing, tracing, and evaluation permissions?
A: They should separate those permissions whenever the gateway handles production prompts, regulated data, or shared model access across teams. The person who routes traffic should not automatically be able to read every trace or approve every evaluation result, because that creates unnecessary privilege overlap.
👉 Read our full editorial: LLM gateways expose new identity and governance gaps in AI stacks
